Classification of diabetes disease using decision tree algorithm (C4.5)

In a close combat situation several types of non-verbal communication are available. However these signals have limits of range and reliability, particularly when line of sight is disrupted. This paper proposes the system for troops to interpret hand and arm military gestures applicable in close combat scenario. In the proposed system, signals are transmitted through secured Bluetooth connections and interpreted at the receiver end. k-NN algorithm, Lookup Table (LuT) and Decision Tree algorithm are used to determine the exact classification of the gestures. This paper presents a system keeping only one fellow trooper in picture and reported 94.6 percent accuracy of the military gestures interpretation.

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Application of Decision Tree Algorithm in Lumber Hierarchies

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.466-467.308 ◽

2012 ◽

Vol 466-467 ◽

pp. 308-313

Author(s):

Dan Guo

Keyword(s):

Data Mining ◽

Decision Tree ◽

Inductive Reasoning ◽

Decision Tree Algorithm ◽

Discrete Function ◽

Tree Algorithm ◽

Id3 Algorithm ◽

Noise Data ◽

Construction Model

The decision tree algorithm is a kind of approximate discrete function value method with high precision, construction model of classification of noise data is simple and has good robustness etc, it is currently the most widely used in one of the inductive reasoning algorithms in data mining, extensive attention by researchers. This paper selects the decision tree ID3 algorithm to realize the standardization of lumber level division, to ensure the accuracy of the lumber division, while improving the partition of speed.

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CLASSIFICATION OF ORCHARD CROP USING SENTINEL-1A SYNTHETIC APERTURE RADAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-335-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 335-338 ◽

Cited By ~ 1

Author(s):

H. Sahu ◽

D. Haldar ◽

A. Danodia ◽

S. Kumar

Keyword(s):

Maximum Likelihood ◽

Random Forest ◽

Decision Tree ◽

Decision Tree Algorithm ◽

Random Forest Algorithm ◽

Synthetic Aperture Radar Data ◽

Tree Algorithm ◽

Maximum Likelihood Classifier ◽

Sar Data

Abstract. A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different classifiers that are maximum likelihood classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum likelihood classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47%, 0.47%, 28.3%, 28.3% and 25.5% respectively in all the classification algorithm but root mean square error for maximum likelihood classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum likelihood classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum likelihood classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.

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